{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jupyter Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# https://ipython.org/ipython-doc/3/config/extensions/autoreload.html#module-IPython.extensions.autoreload\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# https://matplotlib.org/stable/users/explain/backends.html\n",
    "# https://github.com/microsoft/vscode-jupyter/wiki/Using-%25matplotlib-widget-instead-of-%25matplotlib-notebook,tk,etc\n",
    "# For zoomable plot\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "from ann_benchmarks import results\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "from ann_benchmarks.plotting.utils import compute_metrics\n",
    "from ann_benchmarks.datasets import get_dataset\n",
    "from ann_benchmarks.plotting.utils import create_linestyles\n",
    "import numpy as np\n",
    "import sklearn.metrics\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot(dataset, m, hc):\n",
    "    res = results.load_all_results(dataset = dataset, count=100,batch_mode=False)\n",
    "    true_dist = np.array(get_dataset(dataset)[0][\"distances\"])[:1000]\n",
    "    dim = np.array(get_dataset(dataset)[0][\"train\"]).shape[-1]\n",
    "    n_bits = int(dim*m)\n",
    "\n",
    "    compute_metrics(true_dist, res, \"all-k-recalls\", \"all-k-precisions\", True)\n",
    "    res.close()\n",
    "\n",
    "    matrics = defaultdict(lambda: defaultdict())\n",
    "\n",
    "    algo_name_maps = {\n",
    "        \"simHash\": \"Lsh(m={m})\",\n",
    "        \"SB-LSH(Faiss)\": \"FaissLSH(n_bits={n_bits})\",\n",
    "        \"USR-LSH\": \"Usr(m={m},T=16)\",\n",
    "    }\n",
    "\n",
    "    for properties, f in results.load_all_results(dataset = dataset, count=100, batch_mode=False):\n",
    "        if algo_name_maps[properties['algo']].format(m=m, n_bits=n_bits) != properties['name']:\n",
    "            continue\n",
    "\n",
    "        name = properties['name']\n",
    "        matrics[name]['algo'] = properties['algo']\n",
    "        matrics[name]['recalls'] = f['metrics']['all-k-recalls']['all-k-recalls'][:]\n",
    "        matrics[name]['precisions'] = f['metrics']['all-k-precisions']['all-k-precisions'][:]\n",
    "    \n",
    "    plt.clf()\n",
    "    plt.figure(figsize=(12, 9))\n",
    "    \n",
    "    plt.grid(b=True, which='major', color='0.65', linestyle='-')\n",
    "    axs = plt.gca()\n",
    "    axs.set_title(f\"Hash Code Dimension = {n_bits}\" ,fontsize = 24)\n",
    "\n",
    "    hardcode_linestyles = {\n",
    "        \"USR-LSH\": (\"orangered\", \"-\", \"o\"),\n",
    "        \"SB-LSH(Faiss)\": (\"mediumpurple\", \"--\", \"x\"),\n",
    "        \"simHash\": (\"Skyblue\", \"--\", \"^\"),\n",
    "    }\n",
    "\n",
    "    def mean_y(algo):\n",
    "        return -np.log(np.array(algo[1][\"precisions\"])).mean()\n",
    "\n",
    "    auc = {}\n",
    "\n",
    "    for name, matric in sorted(matrics.items(), key=mean_y):\n",
    "\n",
    "        color, linestyle, marker = hardcode_linestyles[matric['algo']]\n",
    "\n",
    "        r = matric[\"recalls\"]\n",
    "        p = matric[\"precisions\"]\n",
    "        auc[matric['algo']] = sklearn.metrics.auc(r, p)\n",
    "\n",
    "        l = axs.plot(r, p, label=matric['algo'], color=color, linestyle=linestyle, lw=3)\n",
    "\n",
    "        axs.set_xlabel('Recall', fontsize=22, labelpad=6)\n",
    "        axs.set_ylabel('Precision', fontsize=22, labelpad=6)\n",
    "\n",
    "    if hc == \"low\":\n",
    "        legend_loc = \"upper right\"\n",
    "    else:\n",
    "        legend_loc = \"lower left\"\n",
    "\n",
    "    leg = axs.legend(loc=legend_loc, prop={'size': 26})\n",
    "\n",
    "    plt.savefig(f\"plots/pr/{dataset}.n={n_bits}.eps\", bbox_inches='tight', format='eps')\n",
    "    # plt.show()\n",
    "    return auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2197/1387234377.py:30: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n",
      "  plt.grid(b=True, which='major', color='0.65', linestyle='-')\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n",
      "Computing all K`s recalls \n",
      "Computing all K`s precisions \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<function <lambda> at 0x7fad7a94f940>, {'USR-LSH': defaultdict(<function <lambda>.<locals>.<lambda> at 0x7fad740d81f0>, {'low': defaultdict(None, {'deep-image-96-angular': 0.14417210626932322, 'sift-128-euclidean': 0.15124104493529536, 'glove-100-angular': 0.14369288945670083, 'glove-25-angular': 0.04700480102581369}), 'high': defaultdict(None, {'deep-image-96-angular': 0.5517712935577662, 'sift-128-euclidean': 0.6835944976444331, 'glove-100-angular': 0.7367756756170246, 'glove-25-angular': 0.6060424583502504})}), 'SB-LSH(Faiss)': defaultdict(<function <lambda>.<locals>.<lambda> at 0x7fad7af78280>, {'low': defaultdict(None, {'deep-image-96-angular': 0.11181372558649393, 'sift-128-euclidean': 0.11095936271520959, 'glove-100-angular': 0.11472480091600105, 'glove-25-angular': 0.02684719308623746}), 'high': defaultdict(None, {'deep-image-96-angular': 0.4514425085038605, 'sift-128-euclidean': 0.6338049874161479, 'glove-100-angular': 0.655537878026634, 'glove-25-angular': 0.497931985875082})}), 'simHash': defaultdict(<function <lambda>.<locals>.<lambda> at 0x7fad74026310>, {'low': defaultdict(None, {'deep-image-96-angular': 0.07521816368535549, 'sift-128-euclidean': 0.08307636253127101, 'glove-100-angular': 0.06439306329916217, 'glove-25-angular': 0.014455956936032452}), 'high': defaultdict(None, {'deep-image-96-angular': 0.3901619279145079, 'sift-128-euclidean': 0.6073819122973416, 'glove-100-angular': 0.5875897988699689, 'glove-25-angular': 0.45446495698423856})})})\n"
     ]
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53734e1bcd5a40ec9778e43369ebc8f1",
       "version_major": 2,
       "version_minor": 0
      },
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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='' width=1200.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_args = [\n",
    "    (\"deep-image-96-angular\", 2, \"low\"), (\"deep-image-96-angular\", 8, \"high\"),\n",
    "    (\"sift-128-euclidean\", 2, \"low\"), (\"sift-128-euclidean\", 20, \"high\"),\n",
    "    (\"glove-100-angular\", 2, \"low\"), (\"glove-100-angular\", 20, \"high\"),\n",
    "    (\"glove-25-angular\", 2, \"low\"), (\"glove-25-angular\", 20, \"high\"), \n",
    "]\n",
    "\n",
    "# plot_args = [(\"deep-image-96-angular\", 2, \"low\")]\n",
    "\n",
    "all_aucs = defaultdict(lambda: defaultdict(lambda: defaultdict()))\n",
    "auc_baseline = defaultdict(lambda: defaultdict())\n",
    "improve = defaultdict(lambda: defaultdict(lambda: defaultdict()))\n",
    "\n",
    "for p in plot_args:\n",
    "    aucs = plot(dataset=p[0], m=p[1], hc=p[2])\n",
    "    for algo, auc in aucs.items():\n",
    "        all_aucs[algo][p[2]][p[0]] = auc\n",
    "        if algo == \"simHash\":\n",
    "            auc_baseline[p[2]][p[0]] = auc\n",
    "\n",
    "print(all_aucs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USR-LSH, low:\n",
      "0.144 (+91.67%) & 0.151 (+82.05%) & 0.144 (+123.15%) & 0.047 (+225.16%)\n",
      "USR-LSH, high:\n",
      "0.552 (+41.42%) & 0.684 (+12.55%) & 0.737 (+25.39%) & 0.606 (+33.35%)\n",
      "SB-LSH(Faiss), low:\n",
      "0.112 (+48.65%) & 0.111 (+33.56%) & 0.115 (+78.16%) & 0.027 (+85.72%)\n",
      "SB-LSH(Faiss), high:\n",
      "0.451 (+15.71%) & 0.634 (+4.35%) & 0.656 (+11.56%) & 0.498 (+9.56%)\n",
      "simHash, low:\n",
      "0.075 (+0.00%) & 0.083 (+0.00%) & 0.064 (+0.00%) & 0.014 (+0.00%)\n",
      "simHash, high:\n",
      "0.390 (+0.00%) & 0.607 (+0.00%) & 0.588 (+0.00%) & 0.454 (+0.00%)\n"
     ]
    }
   ],
   "source": [
    "improves = defaultdict(lambda: defaultdict(lambda: defaultdict()))\n",
    "for algo, aucs in all_aucs.items():\n",
    "    for b, auc in aucs.items():\n",
    "        l = []\n",
    "        for data, a in auc.items():\n",
    "            bl = auc_baseline[b][data]\n",
    "            improve = (a-bl)/bl\n",
    "            improves[algo][b][data] = improve\n",
    "            l.append(f\"{a:.3f} (+{improve*100:.2f}%)\")\n",
    "        print(f\"{algo}, {b}:\")\n",
    "        print(*l, sep=\" & \") # print for Latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a30c5b2b8e64ccba461798b8378ace0",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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PW3/iiSfyFP6JiKhRo0a8+uqr0aZNm/j66683ufbpp5/GlClTYt99991inzJlykTz5s03CfsccMABUbly5U3umzZtWp7m2hoceOCBcfPNN8cRRxxRoOcvvPDCqFatWpxxxhkp16699tp8BYAqVKgQffr0ieuuuy4aN26c71lq1qwZr7zySpxwwgnx1ltvbXJt3rx58eCDD8aNN96Y774AAAAAgB2AgDTmz58fV1xxRbz44ouRnZ1d6H477LBDdOrUKV5++WVBILZKdgACYFtUEjsArV+/PmrUqBErV67cpN6yZcuYOHFivvu9+uqrcdJJJ6XUb7vttrjuuusKPOdvDRgwIPr06ZNS39p2AJozZ84m71EKo0ePHvHCCy+k1CdOnBgtW7Ys0XkWLFgQu+++eyxbtmyT+r777huff/55ofsDAAAAQGElcQegzNIeANi6vPTSS7HXXnvF4MGDiyT8ExGxYsWKGDp0aKxbt65I+gEAsHX46aefUsI/EZE2xJMXRx99dMpuPRERM2bMKFC/pCuq8E9ExJ/+9Ke09d/uxLM5RTVPnTp14uyzz06pT5kyJWbPnl0kawAAAADA9kYACPjVoEGDokePHrF48eJc7ylbtmzsscce0a5du+jSpUu0b98+9txzz6hWzW4pAADbm/nz56etN2/evED9KlSoELvuumtK/aeffipQP/6/li1bRu3atVPqM2fOLIVpIrp27Zq2XlrzAAAAAEDSlS3tAYCtw8iRI6N3796xcePGlGvVq1ePvn37xsknnxwtW7bM9Riv7777LiZMmBAffPBBvPbaazF37tziHhsAgFKU24nSO+ywQ4F7Vq1aNaVWpkyZAvfj/2vcuHH8/PPPm9RKK1y10047pa0LewEAAABAwdgBCIh169bFH/7wh7Thn9NPPz1mzJgR99xzT7Rv3z7X8E9ExK677hqnnnpqPPLIIzF79uwYO3ZsnH/++VGlSpXiHB8AgFJSt27dtPVFixYVuOfChQtTavXq1StwP/6/cuXKpdQyM0vnjwXSzRJRevMAAAAAQNL5kzUgnnnmmZg6dWpK/fzzz49nn302atasme+eGRkZ0b59+3j88cdj9uzZcd999+X6h/wAACTTzjvvnDYENH78+AL1W7RoUUyfPj2lfuCBBxaoH5v6/vvvU2r169cvhUnSzxJRevMAAAAAQNIJAAHx8ssvp9QaN24cDzzwQJH0r169elxxxRVRoUKFIukHAMDWo3v37im1F154IVavXp3vXgMHDkzZlbJixYpx3HHHFXg+/uuLL76IBQsWpNSbNm1aCtNEjBgxIqWWkZERu+66aylMAwAAAADJJwAE27n169en/cP3M844IypVqlQKEwEAkCTpgt5z586N6667Ll99pk2bFrfeemtK/YILLojatWsXakYi+vXrl7Z+zDHHlPAkEWvXro1nn302pd66detcj5UDAAAAADavbGkPAJSuefPmxbp161LqLVu2LIVp8iY7Ozu+/vrrmDZtWsyZMydWrFgROTk5kZWVFdWrV4/dd9899tprr6hWrVqRrLdx48aYNWtWTJ06NWbPnh3Lly+PVatWRVZWVtSoUSNq164drVq1KrUPppYsWRITJkyIb7/9NpYsWRLly5ePHXfcMXbaaac48MADo2LFiiU6z5dffhmff/55/PTTT7F69eqoWbNm1K5dO1q2bOkb3QCwDWratGncdtttcdVVV21S/99uknfeeWeUL19+sz0+/PDD6NmzZyxevHiT+l577RW33XZbkc67PZozZ048/vjjKfU99tgjmjdvXuLz/POf/4x58+al1Lt161biswAAAADAtkIACLZz8+fPT1uvXr16yQ6yBdnZ2fHqq6/G4MGD47333osVK1Zs9v6MjIxo0aJFHH744dGzZ898B5omTpwY7777bowaNSo++uijWLly5Raf2XPPPeOoo46Kyy+/PHbeeed8rfdbGRkZKbWcnJxN/nrYsGFx3333xfDhw2PDhg1p+1SqVCkOO+ywuOmmm6JVq1YFmuXmm2+OW265ZZPaTTfdFDfffPOvf71o0aJ46KGHol+/fjF79uxce+22225x3nnnxaWXXmqHKQDYhlx55ZUxd+7cuO+++zapP/DAAzFkyJA477zzonPnzrHnnntG9erVY82aNTFv3rwYP358DB48ON56662Uo7+aNWsWw4cPj8qVK5fkj7JNuuSSS2LVqlUp9auvvrrEZ/nxxx83eR/5P1lZWfH73/++xOcBAAAAgG2FI8BgO/fbD1r+Z8GCBSU8Se5efPHFaNq0aZxyyinxyiuvbDH8E/HfsMzkyZPjnnvuiVatWuU5AHTbbbdF06ZNo3Xr1nH99dfHsGHD8hT+iYj45ptv4oEHHoimTZvGueeem+fn8mvRokVx4oknxhFHHBHvvPNOruGfiIjVq1fH0KFDo02bNnHRRRdFdnZ2kc/zwgsvxJ577hl//etfNxv+iYiYMWNGXHPNNbHHHnvEhAkTinwWAKD03HvvvfH0009HzZo1N6n/+OOPceONN8YhhxwStWvXjnLlykVWVlbsvvvuccYZZ8Sbb765yXvSMmXKxO9///uYMGFCNGjQoKR/jG3OU089Fa+++mpKfb/99os+ffqU6CzZ2dlx1llnxdKlS1Ou3XTTTSn/7AAAAAAAeScABNu53I6tev/990t4klTLly+PU089NU477bT44YcfCtXr008/zdN9//znP2PGjBmFWmvDhg3x1FNPRfv27eO7774rVK/fmjFjRrRp0yZef/31fD2Xk5MTjz76aJx88slFGgK68cYbo0ePHrFo0aJ8PTd79uzo1KlTjB49ushmAQBKX69eveKHH36Ie+65J1q3bp12V8Pc7LLLLnH11VfHN998E4899lhUqVKlGCfdPkycODEuvvjilHq5cuXiqaeeijJlypToPNdee22MGjUqpd62bdu47LLLSnQWAAAAANjWOAIMtnMNGzaMypUrpxwJ8Oyzz8bVV18dzZo1K5W5Fi1aFF27do2JEyfmek9mZmY0btw4ateuHdWqVYulS5fGwoULY+bMmSnHZRXW/9aqUaNGVKtWLTIyMmLp0qXxww8/xOLFi9M+88UXX0TXrl1j0qRJkZWVVegZ5s2bF126dImZM2duUq9cuXLssssuUbt27VizZk3MmjUr5s6dm7bHa6+9Fvfdd1+RHPdwxx13xN/+9reU+k477RT16tWLKlWqxKJFi2Lq1Kmxbt26lPtWrFgRPXv2jC+//HKrO3IOACicChUqRLVq1aJMmTKb3a3w/1qwYEF8++23MXny5Nhll10iM9P3VQpj9uzZcfzxx8eaNWtSrt16660FPh62oPr16xd33313Sr1q1arx7LPPRtmy/ngCAAAAAArDn6jCdq58+fLxu9/9LqW+du3a6Nq1a3z00UclPtOaNWviuOOOyzX8c8ghh8Rzzz0XCxYsiB9++CE++eSTGD58eHzyySfx/fffx+LFi2P48OFx6aWXRsOGDQs0Q/ny5aNLly7xwAMPxPjx42P58uXxww8/xKeffhqjRo2KkSNHxqRJk+KXX36JGTNmxB133BE777xzSp/p06fH+eefX6AZfqtnz56bhH+OO+64GDFiRCxevDi++OKLGDlyZIwdOzbmzJkTX3zxRZx++ulp+9x4440xa9asQs3y/vvvx/XXX//rX9epUyfuvffemDlzZsycOTM+/vjjeP/992Py5MmxaNGi6N+/f9q/F3Pnzt2kDwCQXBs3box77rkndt5557jkkktixIgReQ7/RESsXLkyXnvttejevXvsvffe8eGHHxbjtNu2xYsXx9FHH502FH7SSSfFn/70pxKd5+23344LLrggpZ6RkREDBw6Mpk2blug8AAAAALAtEgACok+fPmnrs2bNikMOOSSOPvroeOWVV2LFihUlMs+1114bY8eOTalXq1YthgwZEmPGjImePXtGrVq10j5frVq16NKlSzz44IMxa9asePnll6N169Z5Wrtx48Zxxx13xLx582L48OFx2WWXRZs2baJy5cq5PrPrrrvGn//85/jqq6/iD3/4Q8r1559/PiZNmpSn9Tfnf8clZGVlxb///e944403onPnzlG+fPmUe/fee+949tln46GHHkq5tmbNmnjyyScLNcuYMWNi48aNERFxwgknxIwZM+KPf/xj7LTTTin37rDDDtG7d++YMGFCNG/ePOX6wIEDY/ny5YWaBwAoXUuXLo3OnTvH1VdfnbI7YoUKFaJbt25x//33x2uvvRajR4+Od999N5555pm47LLLYs8990zpN3Xq1OjQoUPcc889JfUjbDNWrFgRRx99dEyZMiXl2sEHHxyDBg0q0XlGjx4dJ598cqxfvz7l2v333x8nnnhiic4DAAAAANuqjJyiPicHSJycnJxo3br1FkMq5cuXjzZt2kTbtm2jTZs20bp162jatGlkZGQU2SyffvpptGrVKuUIr7p168Z7770XLVq0KLK10snJySn0z3PxxRfHI488skmtV69e8fTTT+e5R24zVKhQIUaPHh1t27bNc68ePXrECy+8sEmtUaNG8eOPP+bp+ZtvvjluueWWtNe6d+8eL774Yp6P6JgyZUq0bNkyZTeAJ598Mvr27ZunHsVh7ty5m+xQdO4fb4kdqlYrtXkAoCjcf9NlJbLOypUro2PHjjFhwoSUa+edd1789a9/jXr16uX6fE5OTgwdOjT+8Ic/xJw5c1KuP/DAA3HZZUX7swwYMCBtCP7777+PJk2aFOlaJWn16tVx1FFHxQcffJByrVWrVjFixIioVq3k3uN8/PHHcfjhh6cNe9966612ggQAAABgq/Xbzw/nzJkTDRo0KMWJtswOQEBkZGTESy+9FLVr197sfevWrYv//Oc/cf/998fpp58ee+yxR9SoUSO6dOkSN998c4wYMSJWrVpVqFluvPHGlPBPRkZGPPvss8Ue/vnfWoV11113pfzL/8UXX4y1a9cWuvcdd9yRr/BPxH8DPL81e/bs+OGHHwo1y0477RT9+vXLc/gnImLfffeN7t27p9THjBlTqFl+a/ny5TF37tw8/5o3b16Rrg8A25PLL788bfjnX//6V/zrX//abPgn4r/vv44//vj47LPP0r7fu/rqq4tkN8Vt3Zo1a+L4449PG/5p0aJFvPfeeyUa/pkwYUJ07do1bfjn+uuvF/4BAAAAgCImAARExH+PsXr//fdjjz32yNdzS5cujffffz9uueWWOOyww6JOnTpx1llnxfDhw/M9w8yZM+Ott95KqZ9//vnRpUuXfPcrLZUrV47TTjttk9ratWvj008/LVTfBg0apD1ibEuaNWsW++23X0p94sSJhZrn6quvjqpVq+b7uR49ehT5LL917733RsOGDfP8q1WrVkW6PgBsLz799NO0R4v+8Y9/jPPOOy9fvXbcccd48803U95frF+/Pv70pz8Vas5t3dq1a6Nbt25p34PvvffeMXz48KhZs2aJzfPZZ5/FEUccEUuXLk25dvXVV8ett95aYrMAAAAAwPZCAAj41T777BMTJkyIyy+/PCpWrFigHitXroxBgwbF4YcfHgceeGB8+OGHeX52yJAhsXHjxk1qGRkZceWVVxZoltLUpk2blNq4ceMK1fOss86K8uXLF+jZ1q1bp9SmTZtW4FnKli0bvXv33ipmAQBKzwMPPJBSq1GjRtx4440F6te4ceO07/1GjBgRX3/9dYF6buvWrVsXJ510Urzzzjsp1/bcc88YMWLEFnf6LEqTJ0+Oww47LBYvXpxy7fLLL4+77rqrxGYBAAAAgO2JABCwiaysrLj//vtjxowZcd1110WTJk0K3GvcuHFx6KGHxrXXXpsS7EnnvffeS6l17Ngxdt999wLPUFrq1KmTUpsxY0ahenbo0KHAzzZt2jSllu4b2XnVqlWr2GGHHQr0bKNGjaJChQqb1NatWxdr1qwp8DwAQOl4++23U2rHHXdcoY6aOvPMM9PWhw0bVuCe26p169ZF9+7d0+6iufvuu8fIkSOjbt26JTbP559/HocddlgsWrQo5drFF18c999/f4nNAgAAAADbm7KlPQCwdWrQoEHcdtttceutt8a4cePi/fffj9GjR8fYsWNj+fLl+ep1xx13xPTp0+PFF1+MjIyMXO+bMGFCSu3QQw/N9+xFacOGDTFy5Mj4+OOPY8qUKfH111/HkiVLYvny5bF8+fLIzs7Oc68lS5YUapa99967wM+mO6qrMAGgwswS8d95fv7555R5Crrz1G9deeWVcf755+f5/nnz5jkGDADyaebMmSn/PY8o/Pu3XXfdNRo0aBBz587dpD5p0qRC9d3WrF+/Pk455ZR48803U67ttttuMXLkyKhfv36JzfPFF1/EYYcdFgsXLky5dsEFF8TDDz9cYrMAAAAAwPZIAAjYrIyMjDjwwAPjwAMPjOuvvz42btwY06ZNi0mTJsWnn34aH330UUyYMCHWrVu32T4vv/xy3HTTTfHXv/417fXFixen/aZwu3btiuTnyK+ffvop/v73v8cLL7yQ9oOtgkh3DEJ+1KxZs8DPVq5cOaVWmB13CjNLRNHP81tZWVmRlZVVZP0AgFS5vUeqV69eoXvXq1cvJQCULliyvfpf+OeNN95IubbrrrvGyJEjo2HDhiU2z5dffhmdO3dO+8/E+eefH48++miJzQIAAAAA2ysBICBfMjMzo1mzZtGsWbM4/fTTIyJi9erV8fbbb8egQYPi9ddfj5ycnLTP/v3vf48zzzwz9thjj5Rrc+bMSftMo0aNim74PHr44Yfj+uuvz/dOR1uycuXKQj1fpUqVIprkv3L7+5QXRT1LROHmAQBK3oYNG9LWy5Yt/P9mlitXLqWWlyNltwfr16+P0047LV5//fWUa02aNImRI0dG48aNS2yer776KtfwT9++feOxxx7b7C6gAAAAAEDRyCztAYDkq1SpUpx00knx6quvxuTJk6Nly5Zp78vOzo5bb7017bUVK1akrdeoUaPI5syLP/3pT3HppZcWefgnQsAFANi21K5dO229KHZPXLBgQZ7X255s2LAhevbsGa+++mrKtZ133jlGjRoVO+20U4nNM3Xq1OjcuXPav199+vSJJ554QvgHAAAAAEqIHYCAIrXvvvvG2LFj46ijjor3338/5fprr70WGzZsSPlm+Pr169P2q1SpUrHMmc7jjz8ed999d9prZcuWjRYtWkT79u2jSZMm0ahRo6hWrVpUrFgxKlasGJmZm+YpJ02aFBdddFFJjA0AUCrq1KmTtj5hwoTo1atXgfv+/PPP8cMPP6TUt/cAUHZ2dpxxxhkxZMiQlGs77bRTjBw5MnbeeecSm2fatGnRqVOnmD9/fsq1s88+O5588knhHwAAAAAoQQJAQJErX758PPfcc9G0adOUnX2WL18e48ePj4MOOmiTeoUKFdL2WrZsWey4447FNuv/LFy4MK699tqUesWKFeO6666LCy64IF8fOuW2oxEAwLYiKysrdt999/j22283qQ8dOjTuvffeAh8F9uqrr6bdKEJDiAAApCRJREFUObFt27YF6rctyM7Ojl69esWLL76Ycq1x48YxcuTI2GWXXUpsnunTp0enTp1i3rx5Kdd69eoV/fr1SwnIAwAAAADFy5/IAcWibt260aNHj7TXpk+fnlLL7aivJUuWFOVYuRo0aFAsXrx4k1qlSpVi9OjR8Ze//CXf3zj/bS8AgG3RUUcdlVL7/vvvo1+/fgXqt2rVqrjttttS6mXKlInDDz+8QD1LU8eOHSMjIyPlV35s3LgxevfuHYMHD0651qhRoxg5cmTsuuuuRTXyFn333XfRqVOnmDt3bsq1M844IwYMGCD8AwAAAAClwJ/KAcXmd7/7Xdr6woULU2qNGjVK+2HItGnTinyudN54442U2g033BBt2rQpUL90PyMAwLamZ8+eaetXXHFFfPLJJ/nq9b8jrmbNmpVy7cgjj8w1ML4ty8nJiXPPPTeeeeaZlGsNGzaMkSNHxm677VZi88ycOTM6d+4cs2fPTrnWs2fPGDhwoPAPAAAAAJQSR4ABxaZu3bpp6xs2bEipVapUKXbdddeYMWPGJvWxY8fmupNQUZowYUJK7eyzzy7SfgAA25r27dvHiSeeGK+99tom9VWrVkXnzp3jkUceiV69em1x15sff/wxzjrrrBg1alTKtczMzPj73/9ehFMnx6WXXhr9+/dPqVeoUCFuv/32WLhwYZEEz9u3b7/FexYsWBCdO3eOmTNnplzbb7/94qKLLsp36Cud2rVrl2ioCQAAAAC2FQJAQLFZtmxZ2npuwaD27dunBICGDRtW5HP91qpVq2LFihWb1LKysqJhw4YF7jlmzJjCjgUAkAj33HNPjBkzJhYtWrRJfcWKFXH22WfHnXfeGX369IlDDz00dt1116hRo0asXr06fv755/jkk0/izTffjBdeeCHWr1+ftv/ll18eLVq0yPM8AwYMiD59+hToZ9lll122eM/3338fTZo0KVD//Bo6dGja+tq1a+Oss84qsnVycnK2eM9XX30V3333XdprkydPjkMOOaRIZjn77LNjwIABRdILAAAAALYnAkBAsZk6dWraem4BoGOOOSaeffbZTWpff/11jBw5Mjp16lTk8/3PkiVLUmo77LBDgft98MEH8e233xZiIgCA5Nhtt93i3//+d3Tp0iVWrlyZcv2rr76Kq6++ukC9e/ToEffcc09hRwQAAAAA2OZllvYAwLYpJycnXnzxxZR62bJlo23btmmfOf7446Nq1aop9b/+9a95+lZyQVWrVi2ltnDhwli3bl2B+t1xxx2FHQkAIFHatWsXo0ePjubNmxdJvzJlysR1110XTz/99BaPDwMAAAAAQAAItnvz58+PE044ISZMmFCkfR9//PGYMmVKSv3QQw+NmjVrpn2mSpUqcd5556XUR40aFffff3+RzvfbdX8bPFq/fn2Bjh978skn45133imq0QAAEqNly5YxadKk+Otf/xr169cvUI/MzMw48sgj46OPPorbbrstypUrV8RTAgAAAABsmxwBBtu5nJyceOONN+KNN96Io446Kq688sro1KlTZGYWPB84ePDguOyyy9JeO/fcczf77LXXXhv9+/ePX375ZZP6n/70p6hWrVr07du3QDPNnTs3GjRokOv1Dh06xNChQzep3XjjjXH44YdH+fLl87TG22+/HZdeemmB5gMA2BZUqFAh/vKXv8S1114bQ4cOjffeey/Gjx8fU6ZMifXr16d9pl69etGmTZs48MADo2fPntGkSZNCzXDMMcfE2LFjC9Vjc/ITbho1alSh1vrhhx8K9XxR6tixY7HuygkAAAAAFI4AEPCrt99+O95+++1o1KhRnH766dGjR4/Yb7/98hwG+uqrr+Lmm2+Ol156Ke31Dh06xOmnn77ZHrVq1YpHHnkkevbsuUk9Ozs7zj333Jg4cWLcfPPNUadOnTzN9Pnnn8c//vGPGDhwYKxduzbX+0488cSUANCkSZPi5JNPjkGDBqU9Jux/1q9fHw8++GBcd911v36wVaZMmcjOzs7TjAAA25qyZctGt27dolu3bhERsWHDhli8eHEsWbIkli1bFuXLl4/q1atHjRo1YocddijStWvXrh21a9cu0p4AAAAAAFs7ASAgxezZs+Ouu+6Ku+66K6pWrRrt2rWL9u3bR+PGjaNWrVqx4447Rk5OTixfvjzmzp0bU6ZMiQ8++CDtkV//U79+/XjyySfztH6PHj1iwoQJce+996Zc++c//xmDBg2K7t27x5FHHhktW7aM2rVrR1ZWVixbtix+/vnn+Pzzz+OTTz6JN954I7755ps8rdmrV6+49dZb4/vvv9+kPnTo0GjWrFlceOGFceSRR8aee+4ZVapUiUWLFsXMmTPj7bffjmeeeSamT5/+6zMZGRlx3XXXxd/+9rc8rQ0AsK0rW7asYA4AAAAAQDESAAI2a9myZTF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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure 14\n",
       "                </div>\n",
       "                <img src='' width=800.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot delete time\n",
    "\n",
    "matrics = defaultdict(lambda: defaultdict())\n",
    "\n",
    "dataset = \"deep-image-96-angular\"\n",
    "\n",
    "for properties, f in results.load_all_results(dataset = dataset, count=1, batch_mode=False):\n",
    "    matrics[properties['algo']]['delete_time'] = properties['delete_time']\n",
    "\n",
    "# Sorting by mean y-value helps aligning plots with labels\n",
    "def mean_y(algo):\n",
    "    return -np.log(np.array(algo[1][\"delete_time\"])).mean()\n",
    "\n",
    "hardcode_linestyles = {\n",
    "    \"USR-LSH\": (\"orangered\", \"-\", \"o\"),\n",
    "    \"SB-LSH(Faiss)\": (\"mediumpurple\", \"--\", \"x\"),\n",
    "    \"simHash\": (\"Skyblue\", \"--\", \"^\"),\n",
    "}\n",
    "\n",
    "plt.clf()\n",
    "axs = plt.gca()\n",
    "\n",
    "for i, (algo, w) in enumerate(sorted(matrics.items(), key=mean_y)):\n",
    "\n",
    "    if algo in hardcode_linestyles:\n",
    "        color = hardcode_linestyles[algo][0]\n",
    "    else:\n",
    "        color = \"slategrey\"\n",
    "\n",
    "    b = axs.barh(algo, w['delete_time'], color=color)\n",
    "    axs.tick_params(axis='y', which='major', labelsize=14)\n",
    "    bl = axs.bar_label(b, fmt='%.2f', padding=5, fontsize=14)\n",
    "\n",
    "axs.margins(x=0.15)\n",
    "axs.set_xlabel('Deletion Time (seconds)', fontsize=16, labelpad=5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"plots/del/{dataset}.eps\", bbox_inches='tight', format='eps')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
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  "vscode": {
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 },
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}
